1. Field of the Invention
The present invention relates to an image processing apparatus and method, data processing apparatus and method, and program and recording medium, and in particular, to an image processing technique and a data processing technique suitable for reconstructing, interpolating, enlarging and encoding high quality image information which does not exist in image data (low image quality information) before processed.
2. Description of the Related Art
As a method for generating a high resolution output image from a low resolution input image, a technique has been proposed that preliminarily learns pairs of low resolution images and high resolution images with respect to a plurality of the contents of images, acquires a transformational (projective) relationship from low resolution image information to high resolution image information and then generates (reconstructs) an image including high resolution information from a low resolution input image using this projective relationship (JIA Kui, GONG Shaogang “Generalized Face Super-Resolution”, IEEE Transactions of Image Processing, Vol. 17, No. 6, June 2008 Pages 873-886 (2008)).
This method of the related art can be divided into a learning step and a reconstruction step. The preceding learning step preliminarily learns a projective relationship between the low resolution information and the high resolution information about the group of pairs (referred to as “learning image set”) of the low resolution images and the high resolution images using tensor singular value decomposition (TSVD). For instance, tensors representing projective relationships of modality eigenspaces, such as a transformation from a real space of low resolution pixels to a pixel eigenspace, transformation to an individual difference eigenspace of a person (eigenspace), and a transformation further to a high resolution pixel eigenspace, and a transformation from the high resolution pixel eigenspace to the real space, are acquired.
On the other hand, the reconstruction step projects an arbitrary input image of low resolution information including a learning image set to an image of high resolution information using the learned tensor.
This technique is capable of representing the number of variations of the modalities of projective transformations (individual differences of people, facial expressions, resolutions of images, orientations of faces, variations in illumination, human races, etc.) in the ranks of tensors (capable of designing a learning model according thereto), and of reconstruction with high precision by projecting in a state of satisfying the input condition.